This fast-track SBIR will result in a new type of software system, called V-Motive, which will facilitate the combination of two proven autism behavioral interventions: Applied Behavior Analysis therapy (ABA) and Video Modeling (VM). V-Motive will have the potential to change how ABA therapy is performed by making it feasible for therapy providers to leverage VM in a comprehensive fashion within ABA therapy sessions. ABA therapy is currently the most-prescribed autism intervention, characterized by repetitive discrete trials in which an interventionist prompts the child to elicit desired behaviors or to perform steps within the gradual acquisition of target skills (Myers, 2007). VM is a complementary technique that is particularly effective in children with autism, in which children are instructed to imitate what they see in demonstration videos of the desired behaviors or skills to be acquired (Dorwick, 1991). Both ABA therapy and VM have been extensively researched and deemed evidence-based interventions by the National Standards Project (National Autism Center, 2010). Studies have shown that use of VM within ABA therapy can accelerate a child's progress, especially when a child is struggling to master a particular target (Freeman, 2000). This is especially true if the subject in the video is the child himself performing the skill corretly at an earlier date; this is known as video self-modeling (VSM) (Wert, 2003). However, in practice, VM and VSM are rarely used in a comprehensive fashion within ABA therapy, because it is impractical to create and maintain libraries of videos that closely match the steps comprisin their clients' individualized curriculums. Even when matching videos are available, finding the right video on demand and then cueing it up to the correct time point normally requires a significant amount of time and effort on the part of the interventionist, disrupting the flow of th therapy session. V-Motive will overcome this barrier by making it feasible for interventionists to leverage their ongoing therapy sessions to generate custom video repositories that are built around the unique curriculums of their clients. By interfacing with existing tablet-based ABA therapy management products, the proposed V-Motive system will automatically index raw footage obtained during passively-taped therapy sessions, and upload the indexed footage to a video storage and playback system in the cloud. During subsequent sessions, V-Motive will show thumbnails of available video clips depicting successful past performance of each target being worked on, which the interventionist can play for the child on demand, cued to the appropriate time point automatically. The long term goal of this research is to develop a new type of tool that can be wielded by interventionists to accelerate a child's progress through their ABA curriculum by leveraging evidence-based techniques which were previously unfeasible on a comprehensive scale due to the added labor required to practice those techniques using existing methods. By accelerating a child's progress (even by a small fraction), our system will allow therapy providers to extend their services to a greater number of children in need.